Geographic tokenism on editorial boards: a content analysis of highly ranked communication journals
Bibliographic record
Abstract
Abstract Purpose Research posits that the overrepresentation of certain countries from the Global North contributes to the geographical disparity in knowledge production within communication, media and journalism. Our study sets out to understand geographic tokenism in academia by analyzing the editorial boards of 30 highly ranked journals in communication, media, and journalism studies. We sought to explore if certain institutions and academics from underrepresented regions were overrepresented on journal editorial boards. Methodology We content analyzed the members of the editorial boards of 30 highly ranked communication, media and journalism studies journals. From our coded data we were able to identify the individual’s name, role on the editorial board, institutional affiliation, and country of institutional affiliation. Chi square, Pearson’s correlation, and Hierarchical linear modeling were used in analyzing our data. Findings Our study found that institutions and academics affiliated to institutions in the Global South are woefully underrepresented on journal editorial boards. On the other hand, we report an overrepresentation of a small number of institutions and scholars from the Global South across the sampled journals in instances where there is representation from the underrepresented regions on journal editorial boards. Practical implications Our results show that a journal with more diversity on editorial boards and editorial roles is associated with higher journal ranking. Social implications The social implications of our findings rests in the fact that tokenism can impede the diversity of thought that is necessary to move beyond the thorny idea of Western-centered scholarship being considered normative. Originality Whereas previous studies have analyzed editorial boards, our study is unique because it includes institutional and individual level analyses of journal editorial board members in our analysis of geographical disparities in knowledge production.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.069 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.013 | 0.013 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".